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Category: UX

Around 2002 I attended a private party for Google — before its IPO, when it was a small company focused only on search. I struck up a conversation with Larry Page, Google’s brilliant cofounder. “Larry, I still don’t get it. There are so many search companies. Web search, for free? Where does that get you?” My unimaginative blindness is solid evidence that predicting is hard, especially about the future, but in my defense this was before Google had ramped up its ad auction scheme to generate real income, long before YouTube or any other major acquisitions. I was not the only avid user of its search site you thought it would not last long. But Page’s reply has always stuck with me: “Oh, we’re really making an A.I.”

I’ve thought a lot about that conversation over the past few years as Google has bought 13 other AI and robotics companies in addition to DeepMind. At first glance, you might think that Google is beefing up its AI portfolio to improve its search capabilities, since search constitutes 80 percent of its revenue. But I think that’s backward. Bather than use AI to make its search better, Google is using search to make its AI better. Every tie you type a query, click on a search-generated link, or create a link on the web, you are training the Google AI. When you type “Easter Bunny” into the image search bar and then click on the most Easter Bunny-looking image, you are teaching the AI what an Easter Bunny looks like. Each of the 3 billion queries that Google conducts each day tutors the deep-learning AI over and over again. With another 10 years of steady improvements to its AI algorithms, plus a thousandfold more data and a hundreds more computing resources, Google will have an unrivaled AI. In a quarterly earning conference call in the fall of 2015, Google CEO Sundar Pichai stated that AI was going to be “a core transformative way by which we are rethinking everything we are doing… We are applying it to all of our products, be it search, be it YouTube and Play etc.” My prediction: By 2026, Google’s main product will not be search but AI.

6. Evaluate tools and systems

7. Understand security and privacy issues

Evaluate what user data and information the digital service will be providing or storing and address the security level, legal responsibilities, privacy issues and risks associated with the service (consulting with experts where appropriate).

9. Use open standards and common platforms

10. Test the end-to-end service

Be able to test the end-to-end service in an environment identical to that of the live version, including on all common browsers and devices, and using dummy accounts and a representative sample of users.

16. Identify performance indicators

Identify performance indicators for the service, including the 4 mandatory key performance indicators (KPIs) defined in the manual. Establish a benchmark for each metric and make a plan to enable improvements.